Name
Abstract | ||
|---|---|---|
We consider camera self-calibration, i.e. the estimation of parameters for camera sensors, in the setting of a visual sensor network where the sensors are distributed and energy-constrained. With the objective of reducing the communication burden and thereby maximizing network lifetime, we propose an energy-efficient approach for self-calibration where feature points are extracted locally at the cameras and efficient descriptions for these features are transmitted to a central processor that performs the self-calibration. Specifically, in this work we use reduced-dimensionality quantized approximations as efficient feature descriptors. The effectiveness of the proposed technique is validated through feature matching, and epipolar geometry estimation which enable self-calibration of the network. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4959967 | ICASSP |
Keywords | Field | DocType |
epipolar geometry estimation,efficient feature descriptors,feature point,camera sensor,feature matching,visual sensor network,camera calibration,efficient description,network lifetime,camera self-calibration,central processor,estimation,geometry,robustness,image sensors,parameter estimation,energy efficient,epipolar geometry,image sensor,computer vision,principal component analysis,sensor network,histograms,feature extraction,calibration,quantization,energy efficiency | Computer vision,Scale-invariant feature transform,Image sensor,Epipolar geometry,Pattern recognition,Computer science,Visual sensor network,Camera auto-calibration,Robustness (computer science),Feature extraction,Camera resectioning,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1520-6149 | 2 | 0.45 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Yu | 1 | 27 | 3.90 |
| Gaurav Sharma | 2 | 640 | 56.64 |